SpiderScan: Practical Detection of Malicious NPM Packages Based on Graph-Based Behavior Modeling and Matching

Open source software (OSS) supply chains have been attractive targets for attacks. One of the significant, popular attacks is realized by malicious packages on package registries. NPM, as the largest package registry, has been recently flooded with malicious packages. In response to this severe security risk, many detection tools have been proposed. However, these tools do not model malicious behavior in a holistic way; only consider a predefined set of sensitive APIs; and require huge manual confirmation effort due to high false positives and binary detection results. Thus, their practical usefulness is hindered.

Authors

Institutions

Publication Details

Published
2024-10-18
DOI
https://doi.org/10.1145/3691620.3695492
Citations
4
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
1.40

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

SpiderScan: Practical Detection of Malicious NPM Packages Based on Graph-Based Behavior Modeling and Matching

Ruisi Wang, Bihuan Chen, Susheng Wu, Yiheng Huang et al.
4 citations
Advanced Malware Detection Techniques
1.40
article

SpiderScan: Practical Detection of Malicious NPM Packages Based on Graph-Based Behavior Modeling and Matching

Ruisi Wang, Bihuan Chen, Susheng Wu, Yiheng Huang, Shan Gao, Xin Peng, Wen Zheng, Zhuotong Zhou, Shuo-Chun Ke
article en
4 citations

Abstract

Open source software (OSS) supply chains have been attractive targets for attacks. One of the significant, popular attacks is realized by malicious packages on package registries. NPM, as the largest package registry, has been recently flooded with malicious packages. In response to this severe security risk, many detection tools have been proposed. However, these tools do not model malicious behavior in a holistic way; only consider a predefined set of sensitive APIs; and require huge manual confirmation effort due to high false positives and binary detection results. Thus, their practical usefulness is hindered.

Huawei Technologies (China) (CN), Fudan University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 18%
Advanced Malware Detection Techniques
1.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.